In recent times, deep learning paradigms for intelligent fault diagnosis have become prominent because they can automatically learn fault characteristics from the data without direct feature extraction. Also, they can handle large amount of industrial data which is one of the drawbacks of traditional machine learning methods and this has helped to reform intelligent fault diagnosis since the 2010s. Li et al. [1]} reviewed the literature on the applications of deep learning methods for fault diagnoses, analyzing the deep learning approaches in relevant publications to point out the advantages, disadvantages, areas of imperfections, and directions for future research.
Although the adoption of deep learning methods has led to many successes, these approaches assume that labeled data is sufficient for training diagnosis models [2]}. However, this assumption is impractical given the working conditions in most industries. The collected data is inadequate as machines seldom develop faults, and more healthy condition data are collected than faults. So, even with deep learning approaches, the collected data are unbalanced and insufficient to train reliable fault diagnosis models. This poses some limitations of using intelligent fault diagnosis in industries.
As mentioned earlier, the lack of fault data during the network training process is generally termed as a small sample problem [3]}. Researchers have come up with three significant ways of solving the small sample problem, which are data augmentation-based, transfer learning/domain adaptation-based, and model-based strategies. The data augmentation and transfer learning-based methods attempt to increase the amount of data by generating similar data from the existing fault data; slightly modified copies of existing fault data are used to create synthetic data for training the neural network, transfer learning-based approaches use pre-trained networks from similar domains to train the new models in a bid to minimize the amount of data required for training.
